How Qualitative Data Analysis Happens: Moving Beyond “Themes Emerged”, Edited by Áine M. Humble and M. Elise Radina, New York, NY: Routledge, 2019. xxix + 237 pp., $42.36 (paperback), ISBN 978-1-138-04467-8.
Bibliographic record
Abstract
This reader-friendly text pulls back the curtain on qualitative research and allows the reader to see the messiness of the data analysis behind the finished product.Editors Áine Humble and M. Elise Radina (2019) have selected a group of diverse researchers who are willing to be vulnerable and allow the reader to see what actually goes on behind the scenes.It is like having a conversation with multiple qualitative social science researchers in which they lay their work out in front of you and explain how they worked their way through the various obstacles they faced while conducting their research.Humble and Radina focused on research being done in the family studies area of the social science disciplines.Despite this rather narrow focus, the attention to detail that the authors give to the procedures and steps they go through make the audience for this text much wider than family studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".